
Finding (latent) trading factors
Financial markets are looking at a growing and broadening range of correlated time series for the operation of trading strategies. This increases the importance of latent factor models, i.e., methods that condense high-dimensional datasets into a low-dimensional group of factors that retain most of their underlying relevant information. There are two principal approaches to finding such factors. The first uses domain knowledge to pick factor proxies up front. The second treats all factors as latent and applies statistical methods, such as principal components, to a comprehensive set of correlated variables. A new paper proposes to combine domain knowledge and statistical methods using penalized reduced-rank regression. The approach promises improved accuracy and robustness.